The present invention relates to the field of medical image processing and especially to image adaptive image processing.
One of the mainstays of modern medicine is the number of available medical imaging techniques. Some of the main medical imaging techniques are X-ray CT (Computed Tomography), MRI (Magnetic Resonance Imaging), ULS (Ultra Sound) and NM (Nuclear Medicine). The physical basis of the acquired image differs between the techniques. In X-ray CT, different structures in the body are differentiated by their different X-ray density; in MRI, different structures (and some functional characteristics) are differentiated by their different type and density of hydrogen bonds; in US, different structures are differentiated by their different ultrasound propagation characteristics; and in NM, differently functioning structures are differentiated by their different metabolic processes.
Images acquired by any of the above imaging techniques are far from perfect. Image quality is constrained by many factors, most notably, the allowed safe radiation levels. In addition, the internal parts of the human body are always in motion, so imaging or image acquisition time is also a limitation on image quality.
In many cases, the acquisition process can be optimized either to enhance certain types of details in the images or to reduce the noise levels in the images. Alternatively, the acquired images are post-processed using well known image processing techniques to enhance image quality parameters. Typically, enhancement of one parameter of image quality comes at the expense of a second parameter, for example, edge detection usually adds noise to the image. In high gradient portions of the image, edge enhancement may add significant artifacts. Other types of image processing also add artifacts to the image which may be mistaken by the diagnostician to be pathological indications. Sometimes, the processing masks pathological indications, which would otherwise be apparent in the image.
One solution to the trade-off between image enhancement and artifact addition is to store several image sets, one for each type of reconstruction or post processing. This solution is problematical in two respects, first, the volume of stored data is significantly increased, and second, the diagnostician must correlate between images to ascertain whether a pathological indication is actually an artifact.
Some image processing techniques, such as median filtering and Sobel filtering, have a built in responsiveness to local texture, and so, generate fewer artifacts than other image processing techniques.
Another solution to the trade-off is to process only regions of interest (ROI) in the image which seem to require special processing. However, using ROIs can add severe artifacts to the image at the ROI border.
Yet another solution to the trade-off is to automatically extract features from the image and to apply specific processing to particular features.
It is an object of some aspects of the present invention to provide a method of post-processing images which is self-limiting to certain portions of the image.
It is another object of some aspects of the present invention to provide a method of post-processing images which generates a minimum of artifacts.
It is yet another object of some aspects of the present invention to provide a method of post-processing images in which portions of the image which correspond to different body tissues are post-processed differently.
In medical imaging, image pixel values often corresponds to an absolute physical quantity, for example, x-ray CT values correspond to tissue density, and NM values correspond to tissue absorption of radioactive materials. The acquired image pixel values represent tissue characteristics, such as x-ray absorption, perfusion and functionality. This is in contrast to most types of non-medical imaging. In photography, for example, an image pixel value corresponds to the amount of light reflected by an image feature, which is usually a value quite different from the reflectivity of the image feature, due to the complexity of the lighting environment, distance, etc.
In X-ray CT, pixel values are known as CT numbers, which are used to identify the tissue corresponding to the pixel. In this context it is useful to note that most body organs are composed of relatively large regions which have a substantially uniform tissue structure. Diseased portions of the tissue usually have a different and/or non-uniform tissue structure. In other imaging modalities, such as MRI, ultrasound and NM, the pixel values can also be used to identify tissue characteristics, as will be described more fully below.
In one preferred embodiment of the invention, the post-processing method uses an image processing technique which operates differently, depending on the pixel value, i.e., on the tissue type. In one example, the image processing technique enhances edges in the bone, but not in muscles or in liver tissue.
Additionally or alternatively, the image processing technique is responsive to local tissue texture. For example, edges are enhanced in portions of the lungs having a low local gradient but not in portions of the lungs having a high local gradient.
Additionally or alternatively, the image processing technique is responsive to boundaries between different tissue types. For example, edges could be enhanced inside muscle but not in the boundary area between muscle and bone.
Further additionally or alternatively, the image processing technique is responsive to characteristics of the boundary between neighboring tissues, such as the width thereof. For example, edges would be enhanced in a narrow boundary, but not in a wide boundary. In another example, edges would be enhanced in a low gradient boundary, but not in a high gradient boundary.
Additionally or alternatively, the image processing technique is responsive to the type of boundary between the two neighboring tissues. For example, edges would be enhanced in the boundary between muscle and fat, but not between muscle and bone.
In one preferred embodiment of the invention, the image processing technique determines the tissue type of a pixel based on the local pixel value, such as by using a look-up table. One example where individual pixel values is important is detecting bone remains in old bones. Alternatively, the image processing technique determines the tissue type based on an average (weighted) pixel value of several neighboring pixels and/or on the local texture and/or on the local variance and/or on other local image measures. Preferably, a minimum portion size is determined based on the noise level of the image. In addition, single pixels located adjacent to tissue boundaries can be classified on a pixel by pixel basis.
Additionally or alternatively, the image processing technique segments the image into single tissue areas. One way of identifying that two regions on an image are actually parts of a single organ uses contiguous image slices. Usually, the two regions will be connected in 3D through the contiguous image slices.
In a preferred embodiment of the invention, after such value-based segmentation, other supplementary techniques may be used to complete the segmentation. These techniques may be manual techniques (operator input) or may be based on artifacts created by the segmentation. In addition, these supplementary techniques may be based on factors other than tissue CT numbers. For example, an operator may indicate a pixel which has been classified as muscle and reclassify it as bone. Such reclassification will preferably extend to all the similarly classified pixels which are connected to the pixel.
The image processing techniques of some embodiments of the present invention result in more medically correct results than previously known techniques since the type of parameter enhancement depends on the tissue. In particular, the enhancement technique can be chosen so that it minimizes false positives or false negatives for a particular tissue type.
Optimizing the tradeoff between artifacts and enhancement on the basis of tissue type, results in an overall improvement of the image quality. The processing for each tissue is optimized to have maximum enhancement and minimum artifacts, resulting in a higher ratio between enhancements and artifacts for this method than for the prior art.
In one preferred embodiment of the invention the adaptive filtering is applied to the image data on a segment by segment basis. In other embodiments of the invention the segmented image is reprojected to form segmented attenuation data. Adaptive filtering is then applied to the attenuation data. The segmented data for the segments is summed, processed (by utilizing convolution or FFT methods known in the art) and then backprojected to form the adaptive filtered image. Alternatively, the separate segmented attenuation data is subject to adaptive filtering and convolution of FFT and then summed after convolution or FFT.
In a further aspect of the invention, filtering of the entire image is performed based on characteristics of the image which are presumed to exist, based on the display characteristics of the image requested by the viewer. This method does not act on the data in segments and thus no (time-consuming) segmentation of the image is required.
This method is based on the assumption that the image information which is important to a viewer of an image can be determined, in great measure, from the width (and to some extent from the level) of the window of CT values which he selects for display. Thus, it is assumed, in this preferred embodiment of the invention, that when a narrow window of CT values is chosen for display, the viewer is interested in determining the boundary of low contrast (generally large) objects. Generally, such objects can be visualized better when a low pass or other smoothing filter is applied to the image.
When a wide range of CT values is chosen for display, the supposition is that the viewer is more interested enhanced viewing of small, relatively higher contrast objects or edges. Thus, in accordance with a preferred embodiment of the invention, when a narrow range of CT values is chosen for display, the displayed image is automatically subjected to an a smoothing filter and when the range of CT values which is displayed is wide, a sharpening filter is applied to the displayed image.
There is therefore provided in accordance with a preferred embodiment of the invention, a method of enhancing a medical image including:
determining at least one physical characteristic of a tissue portion corresponding to a portion of the image; and
applying an image processing technique to the image portion chosen based on the at least one tissue characteristic.
Preferably, the tissue portion is a boundary between two tissue types. Preferably, the at least one characteristic includes the width of the boundary. Additionally or alternatively, the at least one characteristic includes the gradient across of the boundary. Additionally or alternatively, the at least one characteristic includes the types of the tissues forming the boundary. Additionally or alternatively, the at least one characteristic includes tissue type. Additionally or alternatively, the at least one characteristic includes the texture of the image.
In a preferred embodiment of the invention, determining the type includes comparing the average value of the image portion to a table of value ranges, where each value range corresponds to a tissue type.
Additionally or alternatively, the extent of the portion is at least partly based on the rate of change of the characteristic. Additionally or alternatively, the at least one characteristic includes tissue texture.
In a preferred embodiment of the invention, the at least one characteristic includes the local density of the tissue. Preferably, the local density is an average of the densities of tissue surrounding the tissue portion.
Alternatively or additionally, the at least one characteristic includes the local gradient of density in the tissue. Preferably, the local gradient is an average of the gradients in tissue surrounding the tissue portion.
Alternatively or additionally, the at least one characteristic includes the metabolism of the tissue. Alternatively or additionally, the at least one characteristic includes the perfusion of the tissue.
In a preferred embodiment of the invention, the extent of the portion is at least partly based on detection of edges in the image.
Additionally or alternatively, the image processing technique is edge enhancement. Preferably, the amount of edge enhancement is responsive to the determination of the tissue characteristic.
In a preferred embodiment of the invention, the method includes segmenting the image into image portions containing the same tissue type.
In another preferred embodiment of the invention, the image processing technique is optimized for the spatial frequency spectrum of the tissue portion.
In accordance with still another preferred embodiment of the invention, the method includes:
determining at least one physical characteristic of a second tissue portion corresponding to a second portion of the image; and
applying a second image processing technique to the second image portion where the second image processing technique is different from the image processing technique.
Preferably, the second image processing technique is chosen to optimized for the spatial frequency spectrum of the second tissue portion. Additionally or alternatively, the second image processing technique is of a different type from the image processing technique in type. Additionally or alternatively, the image processing technique and the second image processing technique have weights and the second image processing technique is different from the image processing technique in weight.
Alternatively or additionally, the image processing technique creates more severe artifacts than the second image processing techniques when applied to the second image portion.
Preferably, the image includes an X-ray computerized tomography image. Alternatively, the image includes a magnetic resonance image. Alternatively, the image includes an ultrasound image. Alternatively, the image includes a nuclear medicine image.
Preferably, the image portion is an image pixel. Alternatively, the image portion includes a plurality of contiguous image pixels.
In a preferred embodiment of the invention, applying an image processing technique comprises applying the technique to the pixel values of the image. Alternatively, applying an image processing technique comprises applying the technique to image precursor data which is used to construct the portion of the image. Preferably, the precursor data is generated by reprojection of image data from the image portion.
There is further provided, in accordance with a preferred embodiment of the invention, a method of filtering a medical image, having a first range of pixel values, for display, the method comprising:
receiving, from a user, a range of pixel values of the medical image to be displayed;
automatically processing the medical image, wherein the image processing is responsive to the range of pixel values to be displayed;
scaling the range of pixel values to be displayed to fit a range of gray levels to be displayed; and
displaying the processed and scaled image.
Preferably, when the range is relatively narrow, automatically processing comprises smoothing the image or applying a noise reducing filter; and
when the range of values is relatively large, automatically processing comprises applying an edge enhancing filter.
There is further provided apparatus for displaying a medical image comprising:
a user input which receives a range of pixel values to be displayed on a display from a user;
circuitry which receives a medical image having a first range of pixel values and scales a reduced range of said pixel values input into a range of gray level values suitable for display, in accordance with the user inputted range of values;
an image processor which processes the image using a processing method automatically responsive to the magnitude of the reduced range of pixel values; and
a display which receives and displays an image which has been processed by said processor and scaled by said circuitry.